migration pathways
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2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Manzar Fawad ◽  
Nazmul Haque Mondol

AbstractTo mitigate the global warming crisis, one of the effective ways is to capture CO2 at an emitting source and inject it underground in saline aquifers, depleted oil and gas reservoirs, or in coal beds. This process is known as carbon capture and storage (CCS). With CCS, CO2 is considered a waste product that has to be disposed of properly, like sewage and other pollutants. While and after CO2 injection, monitoring of the CO2 storage site is necessary to observe CO2 plume movement and detect potential leakage. For CO2 monitoring, various physical property changes are employed to delineate the plume area and migration pathways with their pros and cons. We introduce a new rock physics model to facilitate the time-lapse estimation of CO2 saturation and possible pressure changes within a CO2 storage reservoir based on physical properties obtained from the prestack seismic inversion. We demonstrate that the CO2 plume delineation, saturation, and pressure changes estimations are possible using a combination of Acoustic Impedance (AI) and P- to S-wave velocity ratio (Vp/Vs) inverted from time-lapse or four-dimensional (4D) seismic. We assumed a scenario over a period of 40 years comprising an initial 25 year injection period. Our results show that monitoring the CO2 plume in terms of extent and saturation can be carried out using our rock physics-derived method. The suggested method, without going into the elastic moduli level, handles the elastic property cubes, which are commonly obtained from the prestack seismic inversion. Pressure changes quantification is also possible within un-cemented sands; however, the stress/cementation coefficient in our proposed model needs further study to relate that with effective stress in various types of sandstones. The three-dimensional (3D) seismic usually covers the area from the reservoir's base to the surface making it possible to detect the CO2 plume's lateral and vertical migration. However, the comparatively low resolution of seismic, the inversion uncertainties, lateral mineral, and shale property variations are some limitations, which warrant consideration. This method can also be applied for the exploration and monitoring of hydrocarbon production.


2021 ◽  
Author(s):  
Amir Mosavi ◽  
Majid

Identifying the number of oil families in petroleum basins provides practical and valuable information in petroleum geochemistry studies from exploration to development. Oil family grouping helps us track migration pathways, identify the number of active source rock(s), and examine the reservoir continuity. To date, almost in all oil family typing studies, common statistical methods such as principal component analysis (PCA) and hierarchical clustering analysis (HCA) have been used. However, there is no publication regarding using artificial neural networks (ANNs) for examining the oil families in petroleum basins. Hence, oil family typing requires novel, not overused and common techniques. This paper is the first report of oil family typing using ANNs as robust computational methods. To this end, a self-organization map (SOM) neural network associated with three clustering validity indices were employed on oil samples belonging to the Iranian part of the Persian Gulf’ oilfields. For the SOM network, at first, ten default clusters were selected. Afterwards, three effective clustering validity coefficients, namely Calinski-Harabasz (CH), Silhouette indexes (SI) and Davies-Bouldin (DB), were operated to find the optimum number of clusters. Accordingly, among ten default clusters, the maximum CH (62) and SI (0.58) were acquired for four clusters. Likewise, the lowest DB (0.8) was obtained for four clusters. Thus, all three validation coefficients introduced four clusters as the optimum number of clusters or oil families. The number of oil families identified in the present report is consistent with those previously reported by other researchers in the same study area. However, the techniques used in the present paper, which have not been implemented so far, can be introduced as more straightforward for clustering purposes in the oil family typing than those of common and overused methods of PCA and HCA.


2021 ◽  
Author(s):  
Majid ◽  
Amir Mosavi

Identifying the number of oil families in petroleum basins provides practical and valuable information in petroleum geochemistry studies from exploration to development. Oil family grouping helps us track migration pathways, identify the number of active source rock(s), and examine the reservoir continuity. To date, almost in all oil family typing studies, common statistical methods such as principal component analysis (PCA) and hierarchical clustering analysis (HCA) have been used. However, there is no publication regarding using artificial neural networks (ANNs) for examining the oil families in petroleum basins. Hence, oil family typing requires novel, not overused and common techniques. This paper is the first report of oil family typing using ANNs as robust computational methods. To this end, a self-organization map (SOM) neural network associated with three clustering validity indices were employed on oil samples belonging to the Iranian part of the Persian Gulf’ oilfields. For the SOM network, at first, ten default clusters were selected. Afterwards, three effective clustering validity coefficients, namely Calinski-Harabasz (CH), Silhouette indexes (SI) and Davies-Bouldin (DB), were operated to find the optimum number of clusters. Accordingly, among ten default clusters, the maximum CH (62) and SI (0.58) were acquired for four clusters. Likewise, the lowest DB (0.8) was obtained for four clusters. Thus, all three validation coefficients introduced four clusters as the optimum number of clusters or oil families. The number of oil families identified in the present report is consistent with those previously reported by other researchers in the same study area. However, the techniques used in the present paper, which have not been implemented so far, can be introduced as more straightforward for clustering purposes in the oil family typing than those of common and overused methods of PCA and HCA.


2021 ◽  
Author(s):  
Teresa Davoli ◽  
Pan Cheng ◽  
Xin Zhao ◽  
Lizabeth Katsnelson ◽  
Raquel Moya ◽  
...  

How cells control gene expression is a fundamental question. The relative contribution of protein-level and transcript-level regulation to this process remains unclear. Here we perform a proteogenomic analysis of tumors and untransformed cells containing somatic copy number alterations (SCNAs). By revealing how cells regulate transcript and protein abundances of SCNA genes, we provide insights into the rules of gene regulation. While gene compensation mainly occurs at the protein level across tumor types, genes gained or lost show surprisingly low protein compensation in lung and high RNA compensation in colon cancer. Protein complex genes have a strong protein-level regulation while non-complex genes have a strong transcript-level regulation. Exceptions are plasma membrane protein complexes showing a very low protein-level regulation. Strikingly, we find a strong negative association between the degree of transcript-level and protein-level regulation across genes and pathways. Moreover, genes participating in the same pathway show similar degree of transcript- and protein-level regulation. Pathways including translation, splicing and mitochondrial function show a stronger protein-level regulation while cell adhesion and migration pathways show a stronger transcript-level regulation. These results suggest that the evolution of gene regulation is shaped by functional constraints and that many cellular pathways tend to evolve a predominant mechanism of gene regulation, possibly due to energetic constraints.


Author(s):  
Yunpo Li ◽  
Nathalie A. Thelemaque ◽  
Helen G. Siegel ◽  
Cassandra J. Clark ◽  
Emma C. Ryan ◽  
...  

2021 ◽  
Vol 8 (12) ◽  
Author(s):  
Philip M. Riekenberg ◽  
Jaime Camalich ◽  
Elisabeth Svensson ◽  
Lonneke L. IJsseldijk ◽  
Sophie M. J. M. Brasseur ◽  
...  

Baleen from mysticete whales is a well-preserved proteinaceous material that can be used to identify migrations and feeding habits for species whose migration pathways are unknown. Analysis of δ 13 C and δ 15 N values from bulk baleen have been used to infer migration patterns for individuals. However, this approach has fallen short of identifying migrations between regions as it is difficult to determine variations in isotopic shifts without temporal sampling of prey items. Here, we apply analysis of δ 15 N values of amino acids to five baleen plates belonging to three species, revealing novel insights on trophic position, metabolic state and migration between regions. Humpback and minke whales had higher reconstructed trophic levels than fin whales (3.7–3.8 versus 3–3.2, respectively) as expected due to different feeding specialization. Isotopic niche areas between baleen minima and maxima were well separated, indicating regional resource use for individuals during migration that aligned with isotopic gradients in Atlantic Ocean particulate organic matter. Phenylanine δ 15 N values confirmed regional separation between the niche areas for two fin whales as migrations occurred and elevated glycine and threonine δ 15 N values suggested physiological changes due to fasting. Simultaneous resolution of trophic level and physiological changes allow for identification of regional migrations in mysticetes.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Barbara Koch ◽  
Shiao Tong Kong ◽  
Özgül Gün ◽  
Hans-Jörg Deiseroth ◽  
Hellmut Eckert

Abstract A comprehensive multinuclear (7Li, 31P, 35Cl, 77Se, 79Br) nuclear magnetic resonance (NMR) study has been conducted to characterize local structural configurations and atomic distributions in the crystallographically disordered solid solutions of composition Li6PS5−x Se x X (0 ≤ x ≤ 1, X = Cl, Br) with the Argyrodite structure. In contrast to the situation with the corresponding iodide homologs, there is no structural ordering between the 4a and 4c sites, with the halide ions occupying both of them with close to statistical probabilities. Nevertheless, throughout the composition range, the 16e Wyckoff sites of the Argyrodite structure are exclusively occupied by the chalcogen atoms, forming PY4 3− (Y = S, Se) tetrahedra, indicating the absence of P-halogen bonds. 31P magic-angle spinning (MAS)-NMR can serve to differentiate between the various possible PS4−n Se n 3− tetrahedral units in a quantitative fashion. Compared to the case of the anion-ordered Li6PS5−x Se x I solid solutions, the preference of P–S over P–Se bonding is significantly stronger, but it is weaker than in the halide free solid solutions Li7PS6−x Se x . Each individual PS4−n Se n 3− tetrahedron is represented by a peak cluster of up to five resonances, representing the five different configurations in which the PY4 3− ions are surrounded by the four closest chalcogenide and halide anions occupying the 4c sites; this distribution is close to statistical and can be used to deduce deviations of sample compositions from ideal stoichiometry. Non-linear 7Li chemical shift trends as a function of x are interpreted to indicate that the Coulombic traps created by sulfur-rich PS4−n Se n 3− ions (n ≤ 2) within the energy landscape of the lithium ions are deeper than those of the other anionic species present (i.e., selenium-richer PY4 3− tetrahedra, isolated chalcogenide or iodide ions), causing the Li+ ions to spend on average more time near them. Temperature dependent static 7Li NMR linewidths indicate higher mobility in the present systems than in the previously studied Li6PS5−x Se x I solid solutions. Unlike the situation in Li6PS5−x Se x I no rate distinction between intra-cage and inter-cage ionic motion is evident. Lithium ionic mobility increases with increasing selenium content. This effect can be attributed to the influences of higher anionic polarizability and a widening of the lithium ion migration pathways caused by lattice expansion. The results offer interesting new insights into the structure/ionic mobility correlations in this new class of compounds.


2021 ◽  
pp. 1-67
Author(s):  
Geert de Bruin ◽  
Johan ten Veen ◽  
Martin Wilpshaar ◽  
Noortje Versteijlen ◽  
Kees Geel ◽  
...  

In the Dutch offshore, we have observed numerous acoustic anomalies, usually bright spots, in seismic data of Cenozoic deltaic deposits. When associated with shallow gas, these bright spots are good indicators of resource potential, drilling hazard, or seabed methane emissions. We apply a combined seismic and petrophysical assessment to qualify the bright spots as direct hydrocarbon indicators (DHIs) for shallow gas and to exclude alternative sources of seismic anomalies. In some cases, we use other DHIs such as flat spots, velocity push-downs, transmission, and attenuation effects as estimators for gas saturation. A long-standing discussion concerns the sourcing and migration of shallow gas. Although vertical seismic noise trails (chimneys) tend to be seen as proof that shallow gas originates from the migration of deeper sourced thermogenic gas, the geochemical and isotope analyses almost exclusively indicate that the gas is of microbial origin and generated in situ in the Cenozoic strata. We conclude that the observed “chimneys” are most likely transmission effects, that is, artifacts that do not represent migration pathways of gas. Hence, we believe that for the Dutch offshore, the presence of shallow biogenic gas is not indicative of leakage of deeper thermogenic petroleum plays and cannot be used as an exploration tool for these deeper targets.


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